Department of Business Development and Technology

Autonomous learning model for achieving multi cell load balancing capabilities in HetNet

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

  • Plamen Semov, Technical University of Sofia
  • ,
  • Pavlina Koleva, Technical University of Sofia
  • ,
  • Krasimir Tonchev, Technical University of Sofia
  • ,
  • Vladimir Poulkov, Technical University of Sofia
  • ,
  • Albena Mihovska

Heterogeneous networks (HetNets) have been proposed as a capacity and coverage enabler in LTE-Advanced and beyond communication networks. Their optimal operation requires a significant degree of self-organization. Autonomic Load Balancing (ALB) has been proposed as an important self-organizing (SON) function in the LTE radio access network (RAN). In this work, distributed ALB is achieved by implementing a programmable autonomous learning model. The optimization problem (load balancing) is split into many small optimization problems and tasks, which are solved by using machine learning algorithms. The load conditions of the E-UTRAN NodeB (eNBs) and the measurement reports from the mobile terminals are used for creating a decision map for the load balancing. The simulation results show that by using ALB, the system capacity can be improved significantly.

Original languageEnglish
Journal2016 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2016
DOIs
Publication statusPublished - 14 Apr 2017
Externally publishedYes
Event4th IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2016 - Varna, Bulgaria
Duration: 6 Jun 20169 Jun 2016

Conference

Conference4th IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2016
CountryBulgaria
CityVarna
Period06/06/201609/06/2016

    Research areas

  • autonomic load balancing, machine learning, Self-organisation

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